Essential Guide to Using Data Virtualization for Big Data Analytics

Business is driving the need for better Analytics - historical, real-time, predictive, and cognitive - across a number of domains including customer, product, operations and more to become more competitive. Mirroring that, the data available to companies for such analytics is exploding in volume and complexity. Companies are adopting a myriad technologies from traditional data warehouses, OLAP tools, DW appliances, Big Data / Hadoop systems and streaming real-time analytics platforms to take advantage of these opportunities.

While each specialized analytics platform deliver the most value in specific areas, yet other overall value to the business is maximized when they are combined into an integrated Analytics Platform using Data Virtualization as an essential component of your overall Analytics strategy. It provides cross platform logical views of data and analytic insights across the enterprise and enables flexibility to adapt to new business needs, technology migrations and data sources.

Things you will learn:
- Key business drivers and patterns for Advanced Analytics
- Blueprint for an Integrated Analytics Platform and Logical Data Warehouse
- Combining Best in class Analytics Platforms from IBM with Denodo Data Virtualization
- Successful Use Case Patterns

A lot of things can be simple. And simple can be good in a lot of ways. Because when something is simple, it’s faster. It’s smarter. It’s lighter. It’s stronger. That’s why the IBM PureData System is designed with ‘simple’ in mind. It’s optimized and fine-tuned with built-in expertise for easy delivery of data to today’s demanding applications – leaving the complexity behind. The IBM PureData System – just another way in which simple is still better.

The PureData System for Analytics Mini Appliance gives mid-sized companies rich analytics capabilities without straining their budget, all in a single and easy-to-manage system. As an appliance, it’s data-load-read in hours and is easy to install.

Clients are looking to modernize their data warehouse environments to boost performance, add capacity, meet new analytic requirements and incorporate new technologies to take advantage of big data. Incorporating data warehouse appliances, next generation database technology, and new technologies - like in-memory, stream computing, or Hadoop – help organizations gain new business insights across all data, build confidence in those insights and optimize their warehouse infrastructure.

Data warehouse modernization is about leveraging solutions that build on existing investments rather than replacing them, enabling organizations to analyze more information, deliver insight faster and drive new analytic capabilities.

Join this webcast to learn more about the challenges facing organizations and how you can modernize your data warehouse to capitalize on big data.

Business is driving the need for better Analytics - historical, real-time, predictive, and cognitive - across a number of domains including customer, product, operations and more to become more competitive. Mirroring that, the data available to companies for such analytics is exploding in volume and complexity. Companies are adopting a myriad technologies from traditional data warehouses, OLAP tools, DW appliances, Big Data / Hadoop systems and streaming real-time analytics platforms to take advantage of these opportunities.

While each specialized analytics platform deliver the most value in specific areas, yet other overall value to the business is maximized when they are combined into an integrated Analytics Platform using Data Virtualization as an essential component of your overall Analytics strategy. It provides cross platform logical views of data and analytic insights across the enterprise and enables flexibility to adapt to new business needs, technology migrations and data sources.

Things you will learn:
- Key business drivers and patterns for Advanced Analytics
- Blueprint for an Integrated Analytics Platform and Logical Data Warehouse
- Combining Best in class Analytics Platforms from IBM with Denodo Data Virtualization
- Successful Use Case Patterns

Big data won't deliver big benefits to your organization if you don't trust it. Before you can put it to work, you need to find the information you need, understand it and assess its quality, building confidence. Integrating it with other data, consolidating it into a unified view, managing it across its lifecycle, and preserving its privacy and security can help you harness the power of the data for both analytics and ongoing operations.

Join this webcast to learn how other organizations are doing just that with solutions from IBM InfoSphere. You'll get insights into their strategies as well as the practical steps for getting started with integration and governance for all your data.

eHarmony processes more than 3.5 million matches daily and serves over 51 million registered members. By leveraging IBM PureData System, they were able to decrease processing time from hours to minutes, enabling faster insights into user data and ultimately adding business value.

Bon Ton is able to provide customers with an innovative and convenient shopping experience by understanding what customers want before they walk into the stores thanks to IBM PureData for Analytics. After implementing an IBM Big Data & Analytics solution, Bon Ton is now able to advertise and promote products that their customers want at the price they want.

Data confidence is an abstract notion in most organizations. Yet it is critical in order to make your front line workers trust and act upon big data and analytic insight, as even the most game-changing analytics will have no impact if your team doesn't use them, because they don't trust in the data or the insights. Learn about compelling new research that identifies the critical criteria to measure and score confidence levels in customer data to make better business decisions. You will also learn about a new online tool that provides a fast and easy way for attendees to obtain their score.

Since the eighties, companies have invested millions of dollars designing, implementing and updating enterprise data warehouses as the centerpiece of their business intelligence systems. The founding principle of the data warehouse was the same one we share today: to use data to drive better business decisions. But the data sources have grown, not just in sheer volume, but in varieties outside the domain of traditional data warehousing. To remain competitive in the era of big data, businesses need to augment their data warehouse to achieve faster, more efficient and more scalable analytical processes.

Is your data warehouse a big data building block or a bottleneck? Join us for this special roundtable webcast on optimizing your data warehouse for big data. Don't miss this opportunity to gain a deeper understanding of the key trends driving the future of data management and analysis.

"The increasing prevalence of cloud, mobile, and social technologies is opening the floodgates of data generation and analysis. Leading companies are able to create actionable insight from big data and analytics to deepen client engagement, go after new markets, and respond to the needs of the business faster. They are driving innovation across the enterprise faster, and using cloud at the core of their business strategy to innovate iteratively and with more agility.

Learn about IBM's new and expanded Information Management capabilities now delivered in the cloud, including: Hadoop based analytics, stream processing, in-memory computing, data management, and information integration and governance. Data is critical for a competitive advantage, and clients can take advantage of these new technologies today to deploy and deliver analytics and insights in an agile way, without sacrificing security and privacy. "

Big Data is moving from its early stage as an exciting and important concept, to a more mature stage of implementation. Engineers, managers and architects around the world like yourself are digging into the fundamental questions about what projects to implement, and how.

One of the most important areas being impacted by the Big Data revolution is the Data Warehouse. Enterprises are finding that the traditional ways of capturing, storing and analyzing data are not working given the new data types, volumes, demands for faster turnarounds, and the increase in LOB driven projects.

In this important webcast, Gartner VP and Distinguished Analyst Mark Beyer, and IBM Fellow, VP and CTO of Information Management Tim Vincent, present the context for reimagining the data warehouse, and key concepts in how to think about implementation.

"Big data is all about delivering value from advanced analytics and a single version of truth at all scales. It’s time to discuss the growing role of data warehousing within your big data analytics ecosystem. Hear James Kobielus, IBM Big Data Evangelist, outline the major components, tool suites, databases, and industry approaches for leveraging of data warehouses within the astonishingly innovative big-data universe.

View this webinar to learn how to:
- Build a culture that infuses analytics everywhere
- Invest in a big data & analytics platform
- Be proactive about privacy, security and governance"

Hear veteran Analyst Dr. Robin Bloor explain the pain points associated with modern data volumes and types. In this webinar, Rick Clements of IBM, who touts IBM’s big data platform, will brief Robin specifically on InfoSphere BigInsights, InfoSphere Streams and Watson Explorer. He also presents specific use cases that demonstrate how IT and the line of business can springboard over existing challenges, gain insight and improve operational performance.

Why is IBMer Rick Clements so well informed in regards to the white rhinoceros? Like researchers and environmentalists, he’s been given valuable insight through IBM InfoSphere Streams. And it’s more than just general knowledge about this endangered beast. It’s powerful information and insight that allows nature preservers to stop poachers before they ever strike. Through geospatial positioning, wildlife protection agencies use InfoSphere’s analytical capabilities to predict migration patterns and where those patterns cross with human activity. These agencies can then go on the offensive and stop the poachers from coming anywhere close to this 6,000-pound beast. It’s this exact same predictive capability of InfoSphere Streams that allows retailers to engage customers, monitor their preferences and prevent the competition from swooping in and stealing them away with offers on inferior services or products. So whether its understanding and protecting wildlife or better understanding and serving retail customers, IBM InfoSphere Streams is getting you in touch with the real nature of analytics.

With big data financial and transactional data no longer in silos, we can now look at them together. Vince Walden, Ernst and Young partner, says that big data technologies allow them to look at data from all angles.

Short video preview "The Next Generation of Big Data: New IBM Information Management Cloud Solutions"

The increasing prevalence of cloud, mobile, and social technologies is opening the floodgates of data generation and analysis. Leading companies are able to create actionable insight from big data and analytics to deepen client engagement, go after new markets, and respond to the needs of the business faster. They are driving innovation across the enterprise faster, and using cloud at the core of their business strategy to innovate iteratively and with more agility.

Learn about IBM's new and expanded Information Management capabilities now delivered in the cloud, including: Hadoop based analytics, stream processing, in-memory computing, data management, and information integration and governance. Data is critical for a competitive advantage, and clients can take advantage of these new technologies today to deploy and deliver analytics and insights in an agile way, without sacrificing security and privacy.

The amount of data we generate today from machines, the Internet of Things, social and the plethora of mobile devices is overwhelming. Researchers today are already experimenting with vehicles that produce 250 gigabytes of data an hour! The challenge then is to connect all the data we have. Jerry Cuomo, IBM Fellow in Software Group, declares that "the Internet of Things really binds together these systems of interaction."

This channel enlightens and empowers professionals who are focused on data integration and enterprise data management and are overwhelmed by the growing number of data and data types. These webcasts provide the latest best practices, tips and tricks to help extend their knowledge in big data frameworks.